To help find the right tool, we’ve compared the top five data migration tools to move, transform, and optimize your organization’s data efficiently. Here are our top picks:

1. AWS Database Migration Service: Best for AWS Cloud Migration
2. IBM Informix: Best for Versatile Data Management
3. Matillion: Best for Data Productivity
4. Fivetran: Best for Automated Data Movement
5. Stitch: Best for Versatile Cloud Data Pipelines

Top 5 Data Migration Tools Comparison

Take a look at some of the top data migration tools and their features:

Jump to:

• Key Features of Data Migration Tools
• How to Choose the Best Data Migration Tools for Your Business
• Frequently Asked Questions (FAQs)
• Methodology
• Bottom Line: Choosing the Right Data Migration Tool

AWS Database Migration Service

Best for AWS Cloud Migration

The technology giant Amazon extends data migration services to customers through AWS Database Migration Service. It removes undifferentiated database management tasks to simplify the migration process. This high-performance tool offers the additional advantage of access to other AWS solutions and services. Thus, it is best suited for businesses looking for AWS cloud migration support and features.

Pricing

The AWS Free Tier plan helps users get started with the data migration service for free. See the AWS Pricing Calculator for detailed pricing plans and information.

Features

• Centralized access with AWS Management Console
• Multi-AZ and ongoing data replication and monitoring
• Homogeneous and heterogeneous migration support
• Automated migration planning with AWS DMS Fleet Advisor

Pros

• Simple and easy-to-use service
• Automatic schema assessment and conversion
• Supports migration among 20-plus databases and analytics engines

Cons

• Large-scale data migration can be costly
• Frequent changes in pricing

IBM Informix

Best for Versatile Data Management 

IBM offers data management and migration solutions through an embeddable database: IBM Informix. It is a highly versatile tool that simplifies administration and optimizes database performance. It relies on a hybrid cloud infrastructure. Informix is best for multi-tiered architectures that require device-level processing.

Pricing

IBM Informix Developer Edition is ideal for development, testing, and prototyping and can be downloaded for free. The Informix Innovator-C Edition supports small production workloads and is also freely available. Other editions are available that offer a complete suite of Informix features. Contact the team for their pricing details.

Features

• Real-time analytics for transactional workloads
• High availability data replication (HADR) for mission-critical environments
• Event-driven processing and smart triggers for automated data management
• Silent installation with a memory footprint of only 100 MB

Pros

• Robust processing and integration capabilities
• Minimal administrative requirements
• Native encryption for data protection
• Real-time analytics for fast insights

Cons

• Big data transfers can slow down the platform
• Complex pricing policies

Matillion

Best for Data Productivity

Matillion helps businesses with next-gen ETL (extract, transform, load) solutions for efficient data orchestration. It can automate and accelerate data migration with its universal data collectors and pipelines. With its advanced capabilities, it helps extract full value from a business’s existing infrastructure.

Pricing

Matillion follows a simple, predictable, and flexible pricing model along with free trial versions. It offers Free, Basic, Advanced, and Enterprise editions and pay-as-you-go options. The minimum price for paid plans is $2 per credit. Contact the vendor to speak to an expert for details.

Features

• Change data capture and batch data loading for simplified pipeline management
• Low-code/no-code GUI
• Reverse ETL and prebuilt connectors for easy data sync back
• Drag-and-drop functionality for easier usage

Pros

• Fast data ingestion and integration
• Enterprise assurance
• Post-load transformations
• Customizable configurations

Cons

• High-volume data load can cause crashes
• Support issues
• Needs better documentation

Fivetran

Best for Automated Data Movement

Fivetran offers an efficient platform for data migration. This cloud-based tool relies on a fully-managed ELT architecture that efficiently handles all data integration tasks. It has numerous database replication methods that can manage extremely large workloads.

Pricing

Fivetran offers a 14-day free trial option. It has Free, Starter, Standard, Enterprise, Business Critical, and Private Deployment plans with different features and pricing options. Contact the sales team for specific pricing details.

Features

• More than 300 prebuilt, no-code source connectors
• Quickstart data models for automated transformations
• End-to-end data monitoring with lineage graphs
• Fivetran API for programmatic scaling

Pros

• Flexible connection options for secure deployment
• Advanced role-based access control
• Data catalog integrations for metadata sharing

Cons

• Only cloud-based solutions
• Lacks support for data lakes
• Expensive option for large volumes of data

Stitch

Best for Versatile Cloud Data Pipelines

Stitch offers fully automated cloud data pipelines that can be used without any coding expertise. It helps consolidate data from a vast range of data sources. This enterprise-grade cloud ETL platform is highly trusted for extracting actionable insights.

Pricing

Stitch offers a free trial for two weeks. It follows a transparent and predictable pricing model with no hidden fees. There are three plans: Standard, Advanced, and Premium. The minimum price starts at $100 per month, if billed monthly, or $1,000 if billed annually. Contact the sales team for exact pricing details for each plan.

Features

• 140+ popular data sources
• External processing engines like MapReduce and Apache Spark
• In-app chat support

Pros

• No coding is required
• Centralized, fresh, and analysis-ready data
• Automatically updated pipelines

Cons

• Needs a more friendly user interface
• Customer support issues

Key Features of Data Migration Tools

The primary purpose of using data migration tools is to simplify data transfer across different systems, ensuring integrity and accuracy. Some of the key features they include to accomplish this goal are:

Data Transformation

Data migration tools need to consolidate data from multiple sources, which requires them to have data transformation capabilities. Having a standardized data structure or format across different environments is impossible, but data transformation features can help to make these disparate data sources more manageable and uniform. These tools must optimize data for the destination system, ensuring consistency and coherence. They must also be able to identify inconsistencies or issues and transform data as per target requirements.

Connectors

Data migration tools connect various data sources and targets. Thus, they require various connector modules to help them interact with different systems during a migration. With comprehensive connector coverage, data migration tools can establish a link between the source and targets using required protocols, APIs, or drivers. As a result, data can be efficiently extracted from the source and loaded into the target.

Real-time Analysis

Efficient data migration demands real-time insights for seamless data exchange. Real-time analysis helps in the early detection of errors and accurate data mapping between the source and target. This makes it an essential feature of data migration tools, as it helps with performance monitoring, error detection and prevention, data validation, synchronization, and consistency.

Security and Compliance

Data migrations involve substantial risks like information misuse, unauthorized access, data loss, and corruption. These incidents can lead to severe financial and reputational damages, and may also involve potential legal liabilities. Due to these risks, data migration tools must adhere to strict security and compliance standards to minimize security incidents and other risky outcomes.

Customization

Different businesses have different data requirements. To meet business expectations, data migration tools must offer customization features for changing business requirements. A strong data migration tool will also provide the flexibility and adaptability to help organizations with tailored migration processes.

How to Choose the Best Data Migration Tool for Your Business

Data migrations and similar operations are risky processes, as they involve moving your organization’s sensitive information. Thus, choosing a versatile and reliable tool that ensures a smooth and successful migration is essential.

Here are some key considerations to help select the best data migration tool for specific business needs:

Configuration Type

There are two distinct types of data tool configurations: cloud-based and on-premises. On-premises data tools do not rely on the cloud for data transfer. Instead, they migrate data within the organizational infrastructure, offering full-stack control. These are effective solutions when the business desires to restrict data within its own servers.

Cloud-based data migration tools transfer and store data using cloud platforms on cloud servers. The architecture can be expanded effectively due to the quick availability of resources. These tools also facilitate data migration from on-premises to cloud systems. In addition, they are highly secure and cost-effective.

Enterprise Cloud Migration Services

Choosing enterprise-focused cloud migration services can give you an additional edge. Data migration services that are specifically designed for enterprises can more effectively take care of industry standards and maintain top-notch IT infrastructure. Besides, they offer constant updates based on the latest advancements in technologies and methodologies. They can handle complex business projects with well-designed transformation processes.

Technical Support

When choosing a data migration tool, it is also essential to pay attention to technical support capabilities offered by the vendor. Businesses especially need post-migration support to address any issues. They must also help develop robust backup and recovery strategies to deal with system failures or other potential challenges.

Additional Considerations

There are many different types of data migration, like storage, database, cloud, application, data center, and business process migration. Therefore, you should select the most suitable migration tool based on your business goals and the types of migration you want to complete.

Apart from these aspects, it is also vital that the tool you select integrates efficiently with your current business infrastructure and supports data sources and target systems. This can reduce disruptions and compatibility issues.

Frequently Asked Questions (FAQs)

How Do Data Migration Tools Benefit Businesses?

Data migration tools benefit businesses by streamlining data transfer, storage, and management processes, ensuring accuracy. Since they automate these processes, companies can focus on other essential operational aspects. Also, these tools offer the necessary flexibility and scalability to cater to specific demands.

What Types of Data Can Data Migration Tools Handle?

Data migration tools handle enormous volumes of data in different formats and structures within different systems. They deal with both structured and unstructured data and need to work with databases, enterprise applications, data warehouses, spreadsheets, JSON, XML, CSV, and other file formats.

What Are Open-source Data Migration Tools?

Open-source data migration tools are publicly accessible, typically free-to-use solutions. The source code is available on a central repository and can be customized too. Although they require technically skilled employees for proper implementation and use, community-driven support is a major plus with open-source technology, as you can get assistance from technical experts whenever it’s needed. Therefore, these are ideal options for small-scale projects involving lesser complexities.

Methodology

We implemented a structured research methodology to analyze different data migration tools available in the current marketplace. The research was based on specified evaluation criteria and essential feature requirements.

We evaluated each tool’s real-world performance based on user reviews and performance, as customer satisfaction is crucial. After in-depth analysis with several other criteria, we finally documented the top results for the best data migration tools.

Bottom Line: Choosing the Right Data Migration Tool

Choosing the right data migration tool is crucial for aligning specific business goals. Throughout this article, we explored the top five tools, each with unique strengths. When selecting a data migration solution for your business, consider factors like data complexity, scale, real-time vs. batch processing, security, and compatibility.

Remember, the key to successful data migration lies in aligning your specific business goals with the capabilities offered by your chosen tool. Take the time to evaluate and understand your requirements, consult with stakeholders, and make an informed decision that sets your organization on the path to achieving its desired outcomes.

Also See

Also See Data Migration Trends

As businesses discover new use cases and develop the infrastructure to support more IoT applications, the entire Internet of Things continues to evolve. Let’s look at some of the current trends in that evolution.

Table Of Contents

• Top 5 IoT Trends
  1. IoT Cybersecurity Concerns Grow
  2. IoT Advancements In Health Care
  3. 5G Enables More IoT Opportunities
  4. Demand For Specialized IoT Data Management
  5. Bundled IoT For The Enterprise Buyer
• The Future Of IoT
• Bottom Line: IoT Trends

Top 5 IoT Trends

IoT devices can help companies use their data in many ways, including generating, sharing and collecting data throughout their infrastructure. While some companies are leaping into IoT technology, others are more cautious, observing from the sidelines to learn from the experiences of those pioneering IoT.

When looking through these five key trends, keep in mind how IoT devices affect and interact with company infrastructure to solve problems.

1. IoT Cybersecurity Concerns Grow

As new IoT solutions develop quickly, are users being protected from cyber threats and their connected devices? Gabriel Aguiar Noury, robotics product manager at Canonical, which publishes the Ubuntu operating system, believes that as more people gain access to IoT devices and the attack surface grows, IoT companies themselves will need to take responsibility for cybersecurity efforts upfront.

“The IoT market is in a defining stage,” Noury said. “People have adopted more and more IoT devices and connected them to the internet.” At the same time they’re downloading mobile apps to control them while providing passwords and sensitive data without a clear understanding of where they will be stored and how they will be protected—and, in many cases, without even reading the terms and conditions.

“And even more importantly, they’re using devices without checking if they are getting security updates…,” Noury said. “People are not thinking enough about security risks, so it is up to the IoT companies themselves to take control of the situation.”

Ben Goodman, SVP of global business and corporate development at ForgeRock, an access management and identity cloud provider, thinks it’s important that we start thinking of IoT devices as citizens and hold them accountable for the same security and authorization requirements as humans.

“The evolution of IoT security is an increasingly important area to watch,” Goodman said. “Security can no longer be an afterthought prioritized somewhere after connectivity and analytics in the Internet of Things. Organizations need to start treating the ‘things’ in the Internet of Things as first-class citizens.”

Goodman said such a measure would mean that non-human entities are required to register and authenticate and have access granted and revoked, just like humans, helping to ensure oversight and control.

“Doing this for a thing is a unique challenge, because it can’t enter a username or password, answer timely questions, or think for itself,” he said. “However, it represents an incredible opportunity to build a secure network of non-human entities working together securely.”

For more information on IoT and security: Internet of Things (IoT) Security Trends

2. IoT Advancements In Healthcare

The healthcare industry has benefited directly from IoT advancements. Whether it’s support for at-home patient care, medical transportation, or pharmaceutical access, IoT solutions are assisting healthcare professionals with more direct care in situations where they cannot provide affordable or safe hands-on care.

Leon Godwin, principal cloud evangelist for EMEA at Sungard AS, a digital transformation and recovery company, explained that IoT not only makes healthcare more affordable—it also makes care and treatment more accessible and patient-oriented.

“IoT in healthcare will become more prevalent as healthcare providers look to reduce costs and drive better customer experience and engagement,” Godwin said. “This might include advanced sensors that can use light to measure blood pressure, which could be incorporated in watches, smartphones, or standalone devices or apps that can measure caloric intake from smartphone cameras.”

Godwin said that AI is also being used to analyze patient data, genetic information, and blood samples to create new drugs, and after the first experiment using drones to deliver organ transplants across cities happened successfully, rollout is expected more widely.

Jahangir Mohammed, founder and CEO of Twin Health, a digital twin company, thinks that one of the most significant breakthroughs for healthcare and IoT is the ability to constantly monitor health metrics outside of appointments and traditional medical tests.

“Recent innovations in IoT technology are enabling revolutionary advancements in healthcare,” Mohammed said. “Until now, individual health data has been mostly captured at points in time, such as during occasional physician visits or blood labs. As an industry, we lacked the ability to track continuous health data at the individual level at scale.

“Advancements in IoT are shifting this paradigm. Innovations in sensors now make it possible for valuable health information to be continuously collected from individuals.

Mohammed said advancements in AI and Machine Learning, such as digital twin technology and recurrent neural networks, make it possible to conduct real-time analysis and see cause-and-effect relationships within incredibly complex systems.

Neal Shah, CEO of CareYaya, an elder care tech startup, cited a more specific use case for IoT as it relates to supporting elders living at home—a group that suffered from isolation and lack of support during the pandemic.

“I see a lot of trends emerging in IoT innovation for the elderly to live longer at home and avoid institutionalization into a nursing home or assisted living facility,” Shah said. Through research partnerships with university biomedical engineering programs, CareYaya is field testing IoT sensors and devices that help with everything from fall prevention to medication reminders, biometric monitoring of heart rate and blood pressure—even mental health and depression early warning systems through observing trends in wake-up times.

Shah said such IoT innovations will improve safety and monitoring and make it possible for more of the vulnerable elderly population to remain in their own homes instead of moving into assisted living.

For more information on health care in IoT: The Internet of Things (IoT) in Health Care

3. 5G Enables More IoT Opportunities

5G connectivity will make more widespread IoT access possible. Currently, cellular companies and other enterprises are working to make 5G technology available in more areas to support further IoT development.

Bjorn Andersson, senior director of global IoT marketing at Hitachi Vantara, a top-performing IoT and  IT service management company, explained why the next wave of wider 5G access will make all the difference for new IoT use cases and efficiencies.

“With commercial 5G networks already live worldwide, the next wave of 5G expansion will allow organizations to digitize with more mobility, flexibility, reliability, and security,” Andersson said. “Manufacturing plants today must often hardwire all their machines, as Wi-Fi lacks the necessary reliability, bandwidth, or security.”

But 5G delivers the best of two worlds, he said—the flexibility of wireless with the reliability, performance, and security of wired networks. 5G provides enough bandwidth and low latency to have a more flexible impact than a wired network, enabling a whole new set of use cases.

Andersson said 5G will increase the feasibility of distributing massive numbers of small devices that in the aggregate provide enormous value with each bit of data.

“This capacity to rapidly support new apps is happening so early in the deployment cycle that new technologies and infrastructure deployment can happen almost immediately, rather than after decades of soaking it in,” he said. “With its widespread applicability, it will be feasible to deliver 5G even to rural areas and remote facilities far more quickly than with previous Gs.”

For more: Internet of Things (IoT) Software Trends

4. Demand For Specialized IoT Data Management

With its real-time collection of thousands of data points, the IoT solutions strategy focuses heavily on managing metadata about products and services. But the overwhelming amount of data involved means not all IoT developers and users have begun to fully optimize the data they can now access.

Sam Dillard, senior product manager of IoT and edge at InfluxData, a data platform provider for IoT and in-depth analytics use cases, believes that as connected IoT devices expand globally, tech companies will need to find smarter ways to store, manage and analyze the data produced by the Internet of Things.

“All IoT devices generate time-stamped (or time series) data,” Dillard said. “The explosion of this type of data, fueled by the need for more analytics, has accelerated the demand for specialized IoT platforms.”

By 2025, around 60 billion connected devices are projected to be deployed worldwide—the vast majority of which will be connected to IoT platforms, he said. Organizations will have to figure out ways to store the data and make it all sync together seamlessly as IoT deployments continue to scale at a rapid pace.

5. Bundled IoT For The Enterprise Buyer

While the average enterprise buyer might be interested in investing in IoT technology, the initial learning curve can be challenging as IoT developers work to perfect new use cases for users.

Andrew De La Torre, group VP of technology for Oracle Communications at cloud and data management company Oracle, believes that the next big wave of IoT adoption will be in bundled IoT or off-the-shelf IoT solutions that offer user-friendly operational functions and embedded analytics.

Results of a survey of 800 respondents revealed an evolution of priorities in IoT adoption across industries, De La Torre said—most notably, that enterprises are investing in off-the-shelf IoT solutions with a strong desire for connectivity and analytics capabilities built-in.

Because of specific capabilities, commercial off-the-shelf products can extend IoT into other industries thanks to its availability in public marketplaces. When off-the-shelf IoT aligns with industrial needs, it can replace certain components and systems used for general-use practices.

While off-the-shelf IoT is helpful to many companies, there are still risks as it develops—security risks include solution integration, remote accessibility and widespread deployments and usage. Companies using off-the-shelf products should improve security by ensuring that systems are properly integrated, running security assessments, and implementing policies and procedures for acquisitions.

The Future Of IoT

Customer demand changes constantly. IoT services need to develop at the same pace.

Here’s what experts expect the future of Iot development to look like:

Sustainability and IoT

Companies must embrace IoT and its insights so they can pivot to more sustainable practices, using resources responsibly and organizing processes to reduce waste.

There are multiple ways a company can contribute to sustainability in IoT:

• Smart energy management: Using granular IoT sensor data to allow equipment control can eliminate office HVAC system waste and benefit companies financially and with better sustainability practices.
• Extent use style: Using predictive maintenance with IoT can extend the lifespan of a company’s model of manufacturing. IoT will track what needs to be adjusted instead of creating a new model.
• Reusing company assets: Improved IoT information will help a company determine whether it needs a new product by looking at the condition of the assets and use history.

IoT and AI

The combination of Artificial Intelligence (AI) and IoT can cause industries, businesses and economies to function in different ways than either IoT or AI function on their own. The combination of AI and IoT creates machines that have smart behaviors and supports strong decision-making processes.

While IoT deals with devices interacting through the internet, AI works with Machine Learning (ML) to help devices learn from their data.

AI IoT succeeds in the following implementations:

• Managing, analyzing, and obtaining helpful insights from customer data
• Offering quick and accurate analysis
• Adding personalization with data privacy
• Providing assistance to use security against cyber attacks

More Use of IoT in Industries

Healthcare is cited as one of the top IoT industries, but many others are discovering how IoT can benefit their companies.

Agriculture

IoT can be used by farmers to help make informed decisions using agriculture drones to map, image, and survey their farms along with greenhouse automation, monitoring of climate conditions, and cattle monitoring.

IoT enables agriculture companies to have more control over their internal processes while lowering production risks and costs. This will reduce food waste and improve product distribution.

Energy

IoT in the energy sector can improve business performance and customer satisfaction. There are many IoT benefits for energy industry, especially in the following areas:

• Remote monitoring and managing
• Process optimization
• Workload forecasting
• Grid balancing
• Better decision-making

Finance

Banks and customers have become familiar with managing transactions through many connected devices. Because the amount of data transferred and collected is extensive, financial businesses now have the ability to measure risk accurately using IoT.

Banks will start using sensors and data analytics to collect information about customers and offer personalized services based on their activity patterns. Banks will then better understand how their customers handle their money.

Manufacturing

Manufacturing organizations gather data at most stages of the manufacturing process, from product and process assistance through planning, assembly and maintenance.

The IoT applications in the manufacturing industry include:

• Production monitoring: With IoT services’ ability to monitor data patterns, IoT monitoring provides optimization, waste reduction and less mundane work in process inventory.
• Remote equipment management: Remote work has grown in popularity, and IoT services allow tracking and maintaining of equipment’s performance.
• Maintenance notifications: IoT services help optimize machine availability by receiving maintenance notifications when necessary.
• Supply chains: IoT solutions can help manufacturing companies track vehicles and assets, improving manufacturing and supply chain efficiency.

For more industries using IoT: IoT in Smart Cities

Bottom Line: IoT Trends

IoT technology reflects current trends and reaches many areas including AI, security, healthcare, and other industries to improve their processes.

Acknowledging IoT in a business can help a company improve a company structure, and IoT will benefit a company’s infrastructure and applications.

For IoT devices: 85 Top IoT Devices

Particularly with innovations like the cloud, edge computing, Internet of Things (IoT) devices, and streaming, big data has become more prevalent for organizations that want to better understand their customers and operational potential. 

Big Data Trends: Table of Contents

• Real Time Analytics
  
• Stronger Reliance on Cloud Storage
  
• Ethical Customer Data Collection
• AI/ML Powered Automation
• Big Data in Different Industries
• Challenges in Big Data
• Bottom Line: Big Data Trends

Real Time Analytics

Real time big data analytics – data that streams moment by moment – is becoming more popular within businesses to help with large and diverse big data sets. This includes structured, semi-structured, and unstructured data from different sizes of data sets.

With real time big data analytics, a company can have faster decision-making, modeling, and predicting of future outcomes and business intelligence (BI). There are many benefits when it comes to real time analytics in businesses:

• Faster decision-making: Companies can access a large amount of data and analyze a variety of sources of data to receive insights and take needed action – fast.
• Cost reduction: Data processing and storage tools can help companies save costs in storing and analyzing data. 
• Operational efficiency: Quickly finding patterns and insights that help a company identify repeated data patterns more efficiently is a competitive advantage. 
• Improved data-driven market: Analyzing real time data from many devices and platforms empowers a company to be data-driven. Customer needs and potential risks can be discovered so they can create new products and services.

Big data analytics can help any company grow and change the way they do business for customers and employees.

For more on structured and unstructured data: Structured vs. Unstructured Data: Key Differences Explained

Stronger Reliance On Cloud Storage

Big data comes into organizations from many different directions, and with the growth of tech, such as streaming data, observational data, or data unrelated to transactions, big data storage capacity is an issue.

In most businesses, traditional on-premises data storage no longer suffices for the terabytes and petabytes of data flowing into the organization. Cloud and hybrid cloud solutions are increasingly being chosen for their simplified storage infrastructure and scalability.

Popular big data cloud storage tools:

• Amazon Web Services S3
• Microsoft Azure Data Lake
• Google Cloud Storage
• Oracle Cloud
• IBM Cloud
• Alibaba Cloud

With an increased reliance on cloud storage, companies have also started to implement other cloud-based solutions, such as cloud-hosted data warehouses and data lakes. 

For more on data warehousing: 15 Best Data Warehouse Software & Tools

Ethical Customer Data Collection 

Much of the increase in big data over the years has come in the form of consumer data or data that is constantly connected to consumers while they use tech such as streaming devices, IoT devices, and social media. 

Data regulations like GDPR require organizations to handle this personal data with care and compliance, but compliance becomes incredibly complicated when companies don’t know where their data is coming from or what sensitive data is stored in their systems. 

That’s why more companies are relying on software and best practices that emphasize ethical customer data collection.

It’s also important to note that many larger organizations that have historically collected and sold personal data are changing their approach, making consumer data less accessible and more expensive to purchase. 

Many smaller companies are now opting into first-party data sourcing, or collecting their own data, not only to ensure compliance with data laws and maintain data quality but also for cost savings.

AI/ML-Powered Automation

One of the most significant big data trends is using big data analytics to power AI/ML automation, both for consumer-facing needs and internal operations. 

Without the depth and breadth of big data, these automated tools would not have the training data necessary to replace human actions at an enterprise.

AI and ML solutions are exciting on their own, but the automation and workflow shortcuts that they enable are business game-changers. 

With the continued growth of big data input for AI/ML solutions, expect to see more predictive and real-time analytics possibilities in everything from workflow automation to customer service chatbots.

Big Data In Different Industries 

Different industries are picking up on big data and seeing many changes in how big data can help their businesses grow and change. From banking to healthcare, big data can help companies grow, change their technology, and provide for their data.

Banking

Banks must use big data for business and customer accounts to identify any cybersecurity risk that may happen. Big data also can help banks have location intelligence to manage and set goals for branch locations.

As big data develops, big data may become a basis for banks to use money more efficiently.

Agriculture

Agriculture is a large industry, and big data is vital within the industry. However, using the growing big data tools such as big data analytics can predict the weather and when it is best to plant or other agricultural situations for farmers.

Because agriculture is one of the most crucial industries, it’s important that big data support it, and it’s vital to help farmers in their processes. 

Real Estate And Property Management 

Understanding current property markets is necessary for anyone looking, selling, or renting a place to live. With big data, real estate firms can have better property analysis, better trends, and an understanding of customers and markets.

Property management companies are also utilizing their big data collected from their buildings to increase performance, find areas of concern, and help with maintenance processes.

Healthcare

Big data is one of the most important technologies within healthcare. Data needs to be collected from all patients to ensure they are receiving the care they need. This includes data on which medicine a patient should take, their vitals are and how they could change, and what a patient should consume. 

Going forward, data collection through devices will be able to help doctors understand their patients at an even deeper level, which can also help doctors save money and deliver better care.

Challenges in Big Data

With every helpful tool, there will be challenges for companies. While big data grows and changes, there are still challenges to solve.

Here are four challenges and how they can be solved:

Misunderstanding In Big Data

Companies and employees need to know how big data works. This includes storage, processing, key issues, and how a company plans to use the big data tools. Without clarity, properly using big data may not be possible.

Solutions: Big data training and workshops can help companies let their employees learn the ins and outs of how the company is using big data and how it benefits the company.

Data Growth

Storing data properly can be difficult, given how constantly data storehouses grow. This can include unstructured data that cannot be found in all databases. As data grows, it is important to know how to handle the data so the challenge can be fixed as soon as possible.

Solutions: Modern techniques, such as compression, tiering, and deduplication can help a company with large data sets. Using these techniques may help a company with growth and remove duplicate data and unwanted data.

Integrating Company Data

Data integration is necessary for analysis, reporting, and BI. These sources may contain social media pages, ERP applications, customer logs, financial reports, e-mails, presentations, and reports created by employees. This can be difficult to integrate, but it is possible.

Solutions: Integration is based on what tools are used for integration. Companies need to research and find the correct tools.

Lack Of Big Data Professionals

Data tools are growing and changing and often need a professional to handle them, including professionals with titles like data scientists, data analysts, and data engineers. However, some of these workers cannot keep up with the changes happening in the market.

Solutions: Investing money into a worker faced with difficulties in tech changes can fix this problem. Despite the expense, this can solve many problems with companies using big data.

Most challenges with big data can be solved with a company’s care and effort. The trends are growing to be more helpful for companies in need, and challenges will decrease as the technology grows. 

For more big data tools: Top 23 Big Data Companies: Which Are The Best?

Bottom Line: Growing Big Data Trends

Big data is changing continuously to help companies across all industries. Even with the challenges, big data trends will help companies as it grows.

Real time analytics, cloud storage, customer data collection, AI/ML automation, and big data across industries can dramatically help companies improve their big data tools.

• Best for Enterprises: Hewlett Packard Enterprise / Aruba Networks
  
• Best for Cloud Solutions: Arista Networks
  
• Best for Growing Companies: Cisco
  
• Best for Mobility: Cumulus Networks
• Best for Popularity: Dell Technologies
  
• Best for Scalability: Extreme Networks
• Best for SDN: Juniper Networks
  
• Best for Visibility: NETSCOUT
  
• Best for Performance Management: Riverbed Technologies
  
• Best for Versatility: VMware
  
• How to Choose an Enterprise Networking Solution
  
• Bottom Line: Top Enterprise Networking Companies
  

Companies, corporations, and organizations alike can use raw data to collect information about their targets. This, however, requires them to structure and organize the data into a form that’s easier to read and visualize into diagrams and graphs.

This article will help aid you in understanding the various use cases of raw data and how it’s processed by data analysts and scientists. You can also learn more about big data with our library of courses on TechRepublic Academy!

Table of Contents

• How Is Raw Data Used?
• Collecting Raw Data
• How Raw Data Is Processed in 5 Steps
• 8 Examples of Raw Data
• Why Is Raw Data Valuable?

How Is Raw Data Used?

Raw data is data that’s been collected from one or multiple sources but is still in its initial, unaltered state. At this point, and depending on the collection method, the data could contain numerous human, machine, or instrumental errors, or it lacks validation. However, any change that serves to improve the quality of the data is known as processing, and the data is no longer raw.

As a resource, raw data has infinite potential, as it comes in a variety of shapes and types, from databases and spreadsheets to videos and images.

Collecting raw data is the first step toward gaining a more thorough understanding of a demographic, system, concept, or environment. It’s used by business intelligence analysts to extract useful and accurate information about the condition of their business, including audience interest, sales, marketing campaign performance, and overall productivity.

Raw data is often cherished for having limitless potential. That’s because it can be recategorized, reorganized, and reanalyzed in several different ways to yield different results from a variety of perspectives — as long as it’s relevant and has been validated to be credible.

Collecting Raw Data

How data is collected plays a key role in its quality and future potential. Accuracy, credibility, and validity can be the difference between a database of raw data that’s a wealth of information and insights and a waste of space that barely produces any actionable results.

The first and most important step of collecting raw data is to determine the type of information you’re hoping to extract from the database afterward. If it’s userbase and customer information, then online and in-person surveys should focus on a specific age and geographical demographic, whether the process is done in-house or outsourced to a third-party company.

Other types of raw data may require planning in advance. For instance, collecting data from log records would require having a monitoring system in place for anywhere from a few weeks to a year to collect data before being able to pull it.

Second is the collection method. Choosing the appropriate technique can reduce the percentage of human or machine errors you’d have to scrub out when cleaning a raw database. Generally, electronic collecting methods tend to result in lower error rates, as you’d be eliminating the factor of illegibility of handwriting or hard-to-understand accents of slang in the case of audio and video recordings.

Once you’ve determined the source, scope, and methodology, only then does the actual data collection begins. Raw data tends to be large in volume and highly complex, and the actual volume of data acquired can only be estimated during the collection process. An accurate number is only found after the first step of processing the data, which is cleaning it of errors and invalid data points and entries.

How Raw Data Is Processed in 5 Steps

Data analysts, business intelligence tools, and sometimes artificial intelligence (AI) applications, all work together in order to transform raw data into processed and insightful data.

1. Preparing the Data

After acquiring the data through the various collection methods available, you’d then need to prepare it for processing. That’s because raw data, on its own, is considered “dirty,” carrying lots of errors and invalid values. Not to mention, the lack of a homogeneous structure and unification of formats and measuring units, especially if the data comes from a variety of sources or regions.

During data preparation, the data is cleaned, sorted, and filtered according to standard in order to eliminate unnecessary, redundant, or inaccurate data. This step is absolutely essential to ensure high-quality and reliable results from analysis and processing. After all, the results can only be as good and as accurate as the data being fed into the processing tools.

The cleaning step can be simplified or accelerated by using more reliable tools when gathering the data.

2. Inputting the Data

Data inputting, sometimes referred to as data translation, is a step that converts the data into a form that’s machine-readable depending on the tools and software that will, later on, be used in the analysis process.

In the case of digitally collected data, this step is minimal. Though, some structuring and changing of file format might be needed. However, for handwritten surveys, audio recordings, and video clips, it’s important to either manually or digitally extract the data into a form the processing software is capable of understanding.

3. Processing the Data

During this stage, the previously prepared and inputted raw data goes through a number of machine learning and AI-powered statistical data analysis algorithms. Those are responsible for interpreting the data in its raw form into insights and information by searching input for trends, patterns, anomalies, and relationships between the various elements.

This step of the process varies greatly depending on the type of data being processed, whether it comes from an online database, user submissions, system logs, or data lakes. Data scientists and analysts who are well familiar with the data itself and the type of information the organization is looking to extract are capable of fine-tuning and configuring the analysis software as needed.

4. Producing the Output

At this stage, the raw data has been fully transformed into usable and insightful data. It’s translated into a more human-friendly language and can be represented as diagrams, graphs, tables, vector files, or plain text.

This makes it possible to be used in presentations where shareholders and executives with little to no technical skills are able to fully comprehend it.

5. Storing the Data

The results produced by the analysis process should be stored in a safe and accessible location for later use. This is because even processed data can be further analyzed for more details by focusing on a certain area.

This step is critical if the data contains sensitive company information or user data and information. The storage quality needs to be on par with the rest of the company’s data and information, and it must abide by local and applicable data privacy and security laws, such as the GDPR and the CCPA.

Types of Data Processing

There are many data processing methods that can be used depending on the source of the raw data and what it is needed for. The following are six of the various processing types to choose from.

Real-time Data Processing

Real-time data processing allows organizations to extract and output from inputted data in a matter of seconds. This type is best suited for a continuous stream of data rather than an entire database.

Real-time data processing is used most in financial transactions and GPS (global positioning system) tracking.

Batch Data Processing

Batch processing handles data in chunks. The data is collected over a relatively short period of time ranging from daily analysis to weekly, monthly, and quarterly. The result is more accurate than real-time processing, and it’s capable of handling larger quantities of data. That said, it takes more time and is generally more complex to accomplish.

Batch data processing is used in employees’ payroll systems as well as in analyzing short-term sales figures.

Multi-processing

Multi-processing is a time-efficient approach to data processing, in which a single dataset is broken down into multiple parts and analyzed simultaneously using two or more CPUs (central processing units) within a computer system. This type is used for large quantities of raw data that would take an exceptionally long duration to analyze without parallel processing.

Multi-processing is most often used in training machine learning and AI models and in weather-forecasting data.

Distributed Data Processing

Distributed data processing (DDP) is an approach that breaks down datasets too large to be stored on a single machine and distributes them across multiple servers. Using this technique, a single task is shared among multiple computer devices, taking less time to complete and reducing costs for data-reliant businesses.

Thanks to its high fault tolerance, DDP is great for processing raw data from telecommunications networks, peer-to-peer networks, and online banking systems.

Time-sharing Data Processing

Time-sharing data processing allows multiple users and programs to utilize access to the same large-scale CPU unit. This allocation of computer resources allows for the processing of multiple different datasets simultaneously using the same hardware resources.

Time-sharing data processing is mainly used with centralized systems that handle the input and requests of users from multiple endpoints.

Transaction Data Processing

Transaction data processing is used for processing a steady stream of incoming data and sending it back without interruptions. Considering it’s resource-intensive, it’s mostly used on larger server computers responsible for interactive applications.

8 Examples of Raw Data

Raw data is a term that applies to a wide variety of data types. The only criteria for this label are for the data to be in its most crude form and haven’t been under any form of cleaning or processing.

In fact, raw data is more common than you might think, as it allows the utmost freedom and control over the information derived from the database. It can be divided into two categories, quantitative and qualitative data, depending on the values they measure.

Quantitative Raw Data

Quantitative data is raw data that consists of countable data, where each data point has a unique numerical value. This type of data is best used for mathematical calculations and technical statistical analysis.

Some examples of quantitative raw data include:

Customer Information

As long as answers are collected in numerical values or through predetermined multi-choice questions with no room for free answers, this is considered quantitative data. This includes data such as height, age, weight, residential postal code, and level of education.

Sales Records

Records detailing the quantity and frequency of sales of specific goods and services are considered quantifiable data. This can help to determine which variety of products is more popular with customers and at which time of the year.

Combined with customer information, you can even process for more targeted results, such as discovering which particular demographic of customers are most likely to purchase which offering.

Employee Performance

Data on employee performance can include working hours, overall productivity, quality of produced work, and compensation. It can help to calculate the return on investment of your company’s overall staff members, determining whether they’re bringing more financial value than they’re getting paid.

The various metrics, whether submitted through digital or paper surveys by the employees or collected through the internal network and activity monitoring software are quantifiable data.

Revenue and Expenses

Revenue and expenses are strictly quantitative values for a company. Using revenue and expenses data can involve tracking financial activity within an organization, including revenue coming from sold goods and services as well as acquired capital in investment, and comparing it against the expenses of the given period.

This raw data is used to produce the net revenue, which can then be further analyzed to determine which areas of the company have acceptable or unacceptable levels of return on investment.

Qualitative Raw Data

Qualitative data is data that can be recorded and observed in a non-quantifiable and non-numerical nature. It rarely includes numbers and is usually extracted from answers that vary per participant through audio and video recordings, and even one-on-one interviews.

Some examples of qualitative raw data include:

Open-Ended Responses on a Survey

In open-ended survey questions, the respondents are free to structure their own answers instead of choosing one of the predetermined responses. The data cannot be lumped together when it’s raw the same way numbers can be, but it offers a more authentic and insightful view of the thoughts and opinions of the survey takers.

Photographs

While photographs can be categorized in countless ways, there’s a lot of overlap that prevents the use of quantitative measuring methodologies. When training machine learning models for computer vision capabilities, working with raw photographic data is essential.

Customer Reviews

While the 5-star or 10-star rating of a product or service is quantitative data, the reviews left by the customers aren’t. The responses would need to be analyzed on a scale of positive to negative, and highlight the suggestions and pain points experienced by each customer.

News Reports and Public Opinion

Collecting data from news reports and articles that include the name of your company can be a great way to gain an understanding of public opinion. This data is, however, qualitative and cannot be immediately separated into positive and negative coverage, along with the details of praise and criticism mentioned without cleaning and processing the dataset.

Why Is Raw Data Valuable?

Having access to high-quality and reliable raw data serves several purposes, particularly in the realm of business intelligence. It allows experts the chance to access key statistical and predictive analytics to help shape decision-making.

Despite being an experience of trial and error, where not every processing attempt of raw data will result in actionable insights and information, companies can still try to regain and retain as much information as possible from the raw data they input into processing tools.

Some reasons why businesses heavily rely on in-house collected and outsourced raw data sources may include:

• Starting Point: Raw data is the initial source for all data-based decisions on the executive level. It permits you to create compelling charts and graphs of overarching analytical statements about the conditions of the company and anticipated future affairs.
• Data Integrity: Because raw data hasn’t been cleaned, processed, or altered, you can trust that no part of it has been subject to removal or adjustment. This, in return, guarantees more accurate results that haven’t been touched by humans or machines.
• Compatibility With Machine Learning: Machine learning and AI algorithms are incapable of analyzing data after it’s been processed and translated into more human-friendly languages. Datasets are only legible for intelligent models if they’re raw and unaltered.
• Backup Resource: With access to raw data, you can always check your work against it post-processing in case you run into a problem and need to measure findings against the data in its original state.

Raw Data in Business Intelligence

Business intelligence is an overarching concept that combines multiple practices to help better guide the processes of business decision-making through data-based insights and information. It covers business analytics, data visual representation, and data mining, in addition to database management systems and tools.

Raw data is critical in business intelligence, as it offers a reliable source of information. That’s especially important for data-reliant businesses such as healthcare, retail, and manufacturing.

Without accessible raw data, companies are confined to whatever format processed data comes in, and there’s always the risk the data has been processed in error or is misaligned with strategy.

“Any industry has the chance to drive innovation by transforming raw data into gold—if they have the digital tools to do it,” said Ben Gitenstein, vice president of product management at Qumulo and member of the Forbes Technology Council. “File data is growing exponentially, and it’s become increasingly challenging for organizations to manage.

“Retailers are manufacturers in the traditional sense, but they’ve managed to leverage the raw data they have to also become digital manufacturers, updating their services for customers through personalized shopping recommendations and improved supply chains.”

Improving Customer Satisfaction With Insights From Raw Data

Up-to-date raw data is essential in all industries, but especially in fields where the company is capable of further optimizing operations for more profit, fewer costs, and higher levels of customer satisfaction.

You can source data internally by asking existing customers to take a short survey rating their experience with the services or goods your company offers.

Alternatively, you can outsource the work to a data collection company that would target a specific demographic. Either way, raw data that’s specific to your work model and isn’t derived from a large-scale generic database available for free online or prepackaged for sale is the only way to gain direct insights into the opinions and suggestions of customers and clients.

Also, because the data is raw and hasn’t been processed, you can run it through a larger number of processing methodologies and tools to get varying results and standardize your tests. The larger a data sample is and the more expert-level analysis you run, the more familiar you can become with your customers and clients, and shift your business to meet their demands and requests.

Building Valuable Insights Starting With Raw Data

Raw data is data that hasn’t been cleaned, organized, or processed in any capacity. While it can’t directly output information and insights as it is, running it through multiple processing stages can refine it up to a point where insightful graphs, diagrams, and tables can become comprehensible for the average data analyst.

Making use of accurate and up-to-date raw data can be incredibly beneficial, from prompting a data-backed decision-making process and offering unique insights on the inner workings of a system or demographic to its ability to improve the trust of both customers and shareholders.

The Oracle Cloud Region in Chicago is the company’s fourth in the U.S. and serves the Midwest with infrastructure, applications, and data for optimal performance and latency, according to the company last month.

The company hopes that organizations will migrate mission-critical workloads from their own data centers to Oracle Cloud Infrastructure (OCI).

Focus on Security, Availability, Resiliency

The new Chicago Cloud Region will offer over 100 OCI services and applications, including Oracle Autonomous Database, MySQL HeatWave, OCI Data Science, Oracle Container Engine for Kubernetes, and Oracle Analytics. It will pay particular attention to high availability, data residency, disaster protection, and security.

A zero-trust architecture is used for maximum isolation of tenancies, supported by many integrated security services at no extra charge. Its design allows cloud regions to be deployed within separate secure and isolated realms for different uses to help customers fulfill security and compliance requirements. It has a DISA Impact Level 5 authorization for use by U.S. government organizations to store and process Controlled Unclassified Information (CUI) and National Security Systems (NSS) information.

The Chicago Cloud Region contains at least three fault domains, which are groupings of hardware that form logical data centers for high availability and resilience to hardware and network failures. The region also offers three availability domains connected with a high-performance network. Low-latency networking and high-speed data transfer are designed to attract demanding enterprise customers.

Disaster recovery (DR) capabilities harness other Oracle U.S. cloud regions. In addition, OCI’s distributed cloud solutions, including Dedicated Region and Exadata Cloud@Customer, can assist with applications where data proximity and low latency in specific locations are of critical importance.

Oracle has committed to powering all worldwide Oracle Cloud Regions with 100% renewable energy by 2025. Several Oracle Cloud regions, including regions in North America, are powered by renewable energy.

See more: Oracle Opens Innovation Lab in Chicago Market

“Innovation Hub”

Clay Magouyrk, EVP at OCI, said the Midwest is “a global innovation hub across key industries,” as it’s home to over “20% of the Fortune ‘500,’ 60% of all U.S. manufacturing, and the world’s largest financial derivatives exchange.”

“These industries are increasingly seeking secure cloud services to support their need for high-speed data transfer at ultra-low latency,” Magouyrk said.

Samia Tarraf, Oracle Business Group global lead at Accenture, said the Oracle Cloud Region in Chicago will “provide clients with more choices for their cloud deployments.”

“We look forward to collaborating with Oracle to help organizations in the Midwest more quickly and easily harness the business-changing benefits of the cloud,” Tarraf said.

Oracle in the Growing Cloud Market 

Oracle holds an estimated 2% of the cloud infrastructure market, as of Q3 2022, according to Synergy Research Group. AWS holds the first position at 34%.

Gartner numbers put the size of the annual cloud market at somewhere around $400 billion, with growth rates maintaining a steady climb of about 20% or more per year.

Oracle aims to carve out a larger slice of the pie. It now provides cloud services across 41 commercial and government cloud regions in 22 countries on six continents: Africa, Asia, Australia, Europe, North America, and South America.

“The days of the cloud being a one-size-fits-all proposition are long gone, and Oracle recognizes that its customers want freedom of choice in their cloud deployments,” said Chris Kanaracus, an analyst at IDC.

“By continuing to establish cloud regions at a rapid pace in strategic locations, such as the U.S. Midwest, Oracle is demonstrating a commitment to giving its customers as many options as possible to leverage the cloud on their terms.”

See more: Best Cloud Service Providers & Platforms

Telmex is a leading telecommunications and IT services company in Mexico. Similar to Oracle, Telmex works to make investments to develop the top technological platform in the country. Telmex’s IT solutions range from connectivity to cloud services, data centers, cybersecurity, collaboration, and business solutions. 

Telmex-Triara will be the second planned Oracle Cloud region in Mexico, following the Oracle Cloud Querétaro Region, according to Oracle last month.

The estimated 800,000 square-foot data center Telmex-Triara has covered their need to have a host on the second planned Oracle Cloud Region in Mexico.

The partnership grows the cloud regions across Mexico, expanding technology for other Mexican companies.

“One of the main objectives of this alliance is to help our clients in their digital transformation process, offering a complete and differentiated portfolio with the support of leading partners,” says Héctor Slim, CEO, Teléfonos de México.

Slim says the partnership will allow them to expand their cloud services, help “strengthen [their] strategic position”, and “reinforce [their] value proposition”.

“Digital innovation” in Mexico

“We’re pleased to be working with one of Mexico’s largest telecommunications providers to bring OCI to organizations of all sizes and support their digital transformation initiatives. Together we will help boost digital innovation in Mexico and advance the Mexican government’s National Digital Strategy, which seeks to increase interoperability, digital identity, connectivity and inclusion, and digital skills,” said Maribel Dos Santos, CEO and senior vice president, Oracle Mexico.

“One of the main objectives of this alliance is to help our clients in their digital transformation process, offering a complete and differentiated portfolio with the support of leading partners. This agreement with Oracle allows us to expand our cloud services, strengthen our strategic position, and reinforce our value proposition with an industry leader,” Héctor Slim, CEO, Teléfonos de México agrees.

“We are excited to offer our customers, partners, and developers in Mexico access to next-generation cloud services across two planned OCI regions. In partnership with TELMEX-Triara, we will develop new cloud service offerings to jointly help customers successfully move to the cloud,” said Rodrigo Galvão, senior vice president, Technology, Oracle Latin America.

Oracle’s recent deals 

Oracle has recently extended a cloud agreement with AT&T for five years for the use of OCI, Oracle Fusion Cloud Enterprise Resource Planning (ERP), and Oracle Fusion Cloud Customer Experience (CX). 

Oracle has also been appointed to New South Wales (NSW) Government’s Cloud Purchasing Arrangements (CPA) Panel and has renewed a five-year Whole-of-Government (WofG) contract with the state government. This contract expands the cloud beyond Oracle’s United States offices.

Oracle’s recent partners 

• Oracle recently partnered with Subaru to help move their workloads to the Oracle Cloud Infrastructure (OCI).
• Oracle also partners with Exelon Corporation, moving their cloud-first vision forward with OCI. 
• Anaconda, a provider of the world’s most popular data science platform, collaborates with OCI to improve the current processes.
• Oracle and Telefónica formed a global partnership, enabling Telefónica Tech to offer OCI services to clients and professionals.

The growing cloud market 

The global cloud computing market is estimated to grow from $445.3 billion to $947.3 billion from 2021 to 2026, at a compound annual growth rate (CAGR) of 16.3%, according to Markets and Markets.

A company’s need to protect and move and store data and increase business organization leads to the growth of the cloud computing market, the researchers say.

Cloud in Mexico

The adoption of cloud computing in Mexico is growing and can become a driver for data storage in the population and business. 

Cloud computing has followed the evolution of data storage and holds a significant impact on the economy and technology usage.

The revenue in the public cloud market in Mexico is projected to reach $2.81 billion in 2022 and reach $7.22 billion by 2027 showing an annual growth rate (CAGR) of 20.78%, according to Statista.

Redwood City, California-based Equinix aims to help customers scale the launch of their digital products and services. The company serves over 10,000 customers, and they report working with over 260 Fortune “500” companies.

See below to learn all about Equinix’s data center offerings:

See more: The Top Data Storage Companies

Data center portfolio

• Owns and operates over 240 International Business Exchange (IBX) data centers in 70 major metros
• Reports 99.9999% uptime
• Deployment with access to data center expertise from certified technology partners
• Cages, cabinets, and other equipment can be customized and configured to meet customer requirements
• Works to meet certification and compliance standards with network reliability, redundancy, and low latency

IBXflex Space

• Non-cage space that offers security, power, cooling, and interconnecting for office and storage needs
• Unit is equipped with safety and security features, including biometric hand readers, solid doors with pin-proxy cards, fire suppression systems, and smoke detection systems

IBX SmartView

• Real-time online access to environment and operating information relevant to customer footprint at the cage and cabinet levels
• Single source of truth across deployments, including globally
• Actionable, proactive insights using configurable reports and alerts

Equinix Infrastructure Services (EIS)

• Support for planning and project managing deployments
• Vetting, management, and consolidation of all requisite vendor partners into a single invoice with a single Equinix point of contact
• Future-proofing that includes flexible solutions capable of accommodating growth and change

Smart hands

• Remote server access, custom installations, and equipment troubleshooting 24/7/365
• On-site assistance to manage business operations and provide technical support
• Physical audit service to provide information on infrastructure assets and cable connectivity
• Customer portal provides order status, invoices, access reports, and account information, with the ability to place new orders and schedule services
• Payment schedules available with prepayment discounts of up to 40% with rollover of unused monthly plan hours

Equinix Metal

• Developer-friendly automated infrastructure
• DevOps automation offers software deployment in minutes
• Optimize cloud costs, increase security, innovate, and access a world of service providers

Equinix Precision Time

• Time synchronization with 50 microsecond accuracy using Equinix-managed GPS antennas, receivers, grandmaster clocks, and time servers
• Addresses industry-specific challenges, including high-frequency financial services trading platforms needing to precisely order transaction sequences, prevent lip-sync errors for online streaming services, and avoid transactional database errors

Network edge

• Modular infrastructure platform with multi-vendor flexibility
• Reduce complexity and costs with increased ease of management with virtual services

See more: How Storage Hardware is Used by Nationwide, BDO, Vox, Cerium, Children’s Hospital of Alabama, Palm Beach County School District, and GKL: Case Studies

Partners

A commitment to collaboration drives the Equinix partner program, driven by two types of partners:

• Reseller partners: providing end-to-end managed solutions for development, deployment, maintenance, and billing
• Alliance partners: delivering expertise in network optimization and transformation, hybrid and multicloud enablement, and application workload performance

Equinix provides a secure online partner portal known as Partner Central, which offers sales and technical training and sales tracking.

A referral program is also available for partners looking to interconnect with others in the Equinix ecosystem, including:

• Google Cloud
• Microsoft Azure
• Oracle
• AWS
• Cisco
• Dell Technologies
• VMware
• Hewlett Packard Enterprise (HPE)

Data center use case

Zoom has grown to be the leader in enterprise video communications. 

After experiencing unprecedented growth, Zoom needed a data center partner with the infrastructure to support their evolving needs and uptime demands.

Zoom takes advantage of Equinix International Business Exchange (IBX) data centers in nine markets worldwide, plus additional data centers used for disaster recovery backup. Zoom also leverages Equinix Internet Exchange to achieve scalable network-peering aggregation and Equinix Cloud Exchange Fabric (ECX Fabric) to set up and scale access to networks, clouds, partners, and customers.

Zoom identifies several key benefits resulting from their strategic partnership with Equinix:

• Establish data center and interconnection operations quickly and easily
• Establish reliable disaster recovery environments
• High-speed virtual connections with low latency
• Reduce network costs with increased scalability
• Maintenance of regulation-compliant voice and data privacy protections

“Equinix makes everything very easy for us because of the consistency across its global platform. For this reason, and its private interconnection solutions, Equinix is by far our largest data center provider,” says Zak Pierce, data center operations manager, Zoom.

User reviews of Equinix data centers

Reviews of Equinix are consistently high, with many user comments indicating a strong customer focus and high-quality services:

• Gartner Peer Insights: 4.3 out of 5
• G2: 4.1 out of 5

“Partnering with Equinix, a world-class co-location and interconnection service provider, was a faster, better, and cheaper strategy for growing the company, expanding our global footprint and better serving our users with greater performance and reliability.” -Nandu Mahadevan, VP of SaaS operations, BMC Software

“Modernizing our financial services infrastructure on Platform Equinix helps me feel confident we’re ready for whatever the future holds. When we look at our road map, whether it’s microservices or cloud-native solutions, we know we can get where we want to go and bring our key partners with us.” -Richard Hannah, CEO, Celero

“Equinix’s innovative interconnection options allow us to eliminate latency for our customers so that they can process more loans more efficiently.” –Ellie Mae’s VP of cloud infrastructure

Industry recognition of Equinix data center

Equinix is receiving a attention within the industry, with several recent notable achievements:

• Ranked on the Fortune “500” for the first time in 2021
• Awarded the Frost & Sullivan 2022 Singapore Data Center Services Company of the Year
• Named the 2022 HPE GreenLake Momentum Partner of the Year
• Ranked No. 6 on the EPA’s “Top 100” list of green power users

In addition, Forrester analysts predict the interconnection of services, branded as Platform Equinix, offers significant benefits:

• 60%-70% cloud connectivity and network traffic cost reduction
• 30% reduction in latency

Equinix in the data center market

Equinix holds an estimated 11% of the co-location data center revenue and ranks first among the 15 largest providers in 2021, according to Statista. 

Digital Realty holds second place with an estimated 7.6% of the market revenue, with China Telecom in third place at 6.1%.

See more: 5 Top Storage Hardware Trends

Boston-based Iron Mountain serves over 225,000 customers in 58 countries, and they report working with 95% of Fortune “1,000” companies

See below to learn all about the data center offerings by Iron Mountain:

See more: The Top Data Storage Companies

Data center portfolio

With a reputation for regulatory compliance, Iron Mountain offers data centers with many notable features, including:

• 20 locations across three continents
• Scalable options from single data center cabinet to fully dedicated data center
• Cloud-neutral data solutions
• Business continuity
• Physical security
• Environmental sustainability

Smart hands

• Contract all hands-on maintenance tasks
• Responses guaranteed within minutes
• Get help with rack and stack, hardware reboots, cabling, and lock resets
• Experts available on-site and on call 24/7/365
• Anytime, anywhere ordering
• Billed hourly or on a recurring discounted basis

Data center migration

• End-to-end solutions that include analysis, risk assessment, planning, design, execution, and testing
• Clear work statements and methodologies with a single point of contact for support tasks
• Team includes project managers, structured cabling personnel, specialized movers, network engineers, and system administrators
• Ensure budgets and time frames are adhered to

Data center installations and builds

• Use proven experts for site selection, design, construction, government and regulator liaison activities, and the creation of power and network connectivity strategies

Global network operations center (GNOC)

• Central point for customer communication and advocacy
• Log in to manage infrastructure
• Request expert assistance, access logs, power and cooling audits, and bandwidth usage reports
• Monitored ticketing platform

See more: How Storage Hardware is Used by Nationwide, BDO, Vox, Cerium, Children’s Hospital of Alabama, Palm Beach County School District, and GKL: Case Studies

Partners

Iron Mountain has three partnership programs:

• Channel partners: dedicated resources that include training, guidance, sales tools to accelerate growth and create recurring revenue streams
• Real estate brokers: assistance with customers who express the need for new or growing infrastructure requirements, such as a cabinet, cage, or building.
• Strategic alliances: with a wide range of businesses and industry groups, including hardware, software, interconnection, and environmental, help to drive digital infrastructure change.

Data center use case

As an internet services company, hosting over 200,000 websites for 30,000 customers, Krystal needed to find a data center capable of high performance with stability and scalability.

Just as important to Krystal was finding a data center partner that could match their values while meeting its technical demands. 

Iron Mountain proved to be reliable and personal with high environmental standards.

“We loved the fact that there was renewable power being generated on-site, but we also saw Iron Mountain as a longer-term match due to their standards, their ecosystems, and their location spread,” says Alex Easter, CTO, Krystal.

Initially, Krystal planned to build their own data centers but turned to providers as remote work recently expanded. Starting with locations in the U.S., Krystal knew they had plans for data center sites in Europe and Asia Pacific. The global nature of Iron Mountain is one of the reasons Krystal selected the vendor.

User reviews of Iron Mountain data centers

Users at the tech review site Gartner Peer Insights give Iron Mountain an overall rating of 4 out of 5.

Industry recognition of Iron Mountain data centers

Iron Mountain has made a significant commitment to using clean energy while providing data center services.

After joining the U.S. Department of Energy (DOE) Better Building Initiative as a Challenge Partner and demonstrating their ability to run all of their data centers on 100% renewable energy, Iron Mountain won the RE100 Leadership Awards, Most Impactful Pioneer for using clean energy across their global data center infrastructure.

Iron Mountain was also named as a RE100 Key Collaborator, empowering others in the industry.

Iron Mountain in the data center market

Iron Mountain competes primarily against some of the largest data center companies in the world, such as:

• Equinix
• Lumen
• Digital Realty
• CoreSite 

Conclusions

Iron Mountain is a good secure data center option for companies focused on compliance and continuous improvement for design, operation, security, and efficiency. Iron Mountain also provides true global consistency, achieving certifications in information security (ISO 27001), energy management (ISO 50001), and environmental management (ISO 14001).

See more: 5 Top Storage Hardware Trends

IBM will deliver, install, and maintain the high-speed fully solid-state drive (SSD) block storage wherever a customer needs it.

See below to learn all about IBM Storage as a Service and where is stands in the storage sector:

IBM: Storage as a Service and the storage market

IBM’s on-site Storage as a Service solution lands in between two segments of the overall storage market. First, it competes in the global next-generation data storage market that focuses on file and object-based storage and block storage solutions, including hard disk drives (HDDs), SSDs, and tape drives.

Grand View Research estimated a value of $53.1 billion for the global next-generation data storage market in 2019, with a compound annual growth rate (CAGR) of 12.5% from 2019 to 2025 to reach $118.22 billion. This aligns with a projection by KBV Research in the same year, which estimated a CAGR of 12.9% and a market size of $106.3 billion by 2024.

There are several competitors in this market: such as, Dell Technologies; Hewlett Packard Enterprise (HPE); Hitachi Vantara; Micron; NetApp; Pure Storage; and Western Digital.

As an SSD block storage service, IBM’s STaaS offering competes as an operating expense (OpEx) directly against a data center’s capital expense (CapEx) option to purchase other SSD drives. The competitors in this more narrow segment consists of several well-known brands: including, Kingston Technology Company; KIOXIA; Micron; Seagate; and Western Digital.

IBM does not disclose revenue for this category, but in 2020, Allied Market Research forecasted that the value of the SSD market to be $17.85 billion, growing with a CAGR of 10.2% to reach $46.89 billion in 2030. Verified Market Research anticipated a larger market of $27.62 billion the same year, growing at a more robust CAGR of 14.96% and already reaching $84.12 billion by 2028.

IBM Storage as a Service key features

• High capability storage

• • • Latency as low as 50 μs
    • Unlimited capacity
    • Scale capacity up or down as needed
    • 100% guaranteed uptime with HyperSwap configuration installed by IBM Lab Services
    • All-flash and hybrid flash storage options

• Cloud-level service for local data centers

• • Deployable on-premises 
  • Life cycle services manages the storage devices
  • Devices can be upgraded to higher performance tiers
  • Full-service system setup, installation, cabling, and configuring
  • IBM capacity growth monitoring

IBM Storage as a Service key benefits

When data center managers select IBM’s Storage as a Service, they can take advantage of the following benefits:

Cloud flexibility

When adopting cloud resources, data center managers seek unlimited scale and flexibility for capacity and pricing. IBM’s Storage as a Service can start small, grow as large as needed, or shrink if required to provide the flexibility of the cloud for an on-site data center.

No technical debt

When purchasing hard drives, data center managers tend to become locked into technology until it fails or outlives its technical utility. By switching to IBM’s Storage as a Service, data center managers can select the level of quality they need and obtain full system upgrades every three to four years with the same predictable monthly payment.

Full control

Some organizations hesitate to transition to the cloud, because their data is regulated or sensitive. These data centers must maintain full control of the data from the virtual machine (VM) down to the bare metal. Adopting IBM’s Storage as a Service provides flexibility of cloud pricing and scale while maintaining full physical control.

Predictable pricing and cash flow

When building out a data center, obtaining a sufficient number of high-end hard drives can be a huge expense. If these drives need to be purchased outright, accounting rules require the purchase to be recognized as a capital expense, and the purchase ties up large amounts of cash.

By switching to a storage-as-a-service solution, the data center manager avoids tying up cash flow, obtains predictable subscription pricing, and converts the purchase to an operating expense.

IBM Storage as a Service use cases

IBM’s Storage as a Service provides financial flexibility and full-service maintenance for its flagship FlashSystem solutions. Customer stories illustrate the benefits of those FlashSystem products:

Compass Health Brands

Compass Health Brands, a consumer medical product manufacturer, experienced business interruptions and order delays, because its existing storage solutions maxed out the internal storage disk system and could not finish overnight batching jobs.

“It wasn’t getting done until 8 a.m.,” says Andrew Lesak, director of technical services, Compass Health Brands. 

“We get containers from overseas, so if you need to make an adjustment to the shipment, you’ve got to do it ASAP.”

The Compass Health Brands team selected IBM’s storage solutions for their ease of implementation and cost and because the data could transition without shutting down their business.

“We wanted to minimize the impact to the business regarding downtime,” Lesak says. “The IBM FlashSystem solution allowed us to do the data migration while we were live and without any impact to the users — that was huge.”

Electrolux

Home appliance manufacturer Electrolux wanted to focus more on its core business and less on the management of its IT infrastructure. 

However, its existing solution could not keep up with workloads or allow for an integrated, global business insight.

Turning to IBM computing and storage solutions enabled Electrolux to increase their capabilities as well as meet future needs and reduce management burden.

“With data volumes increasing each year, we anticipated that our existing disk storage devices would struggle to meet our future needs, especially as we expected to increase our infrastructure capacity by up to 10% in the years ahead,” says an Electrolux spokesperson. 

“Because IBM FlashSystem solutions offer a rich array of compression tools and low-latency performance, we knew that IBM technologies would be a good option for us in the long-term.”

Honda Pakistan

The joint venture between Honda Motor Company Limited and the Atlas Group of Companies, also known as Honda Pakistan, wanted to grow its dealership network by over 60% but needed a solution that could maintain its high standards for after-sales service as it grew. 

The company recognized it needed to process dealership order data real-time to automatically forecast and replenish spare parts.

By selecting IBM computing and storage solutions, Honda Pakistan increased its computing power with fewer systems and increased the capabilities for their SAP enterprise resource planning (ERP) solution.

“In the past, generating reports on spare parts orders from our dealerships across Pakistan required up to 20 minutes to complete,” says Imran Khan, assistant manager of SAP BASIS, Honda Atlas Cars Pakistan Limited. 

“Since moving our SAP ERP data to IBM FlashSystem, built with IBM Spectrum Virtualize, we’ve slashed these reports down to just four minutes — a reduction of 80%.”

IBM Storage as a Service differentiators

When selecting IBM’s Storage as a Service solution, data center managers often look for the following differentiators:

Brand name

Data center managers understand that IBM continues to provide technical leadership in the data storage and data center computing markets, but they also understand that the non-technical executives will all recognize IBM. Selecting IBM as a vendor minimizes concerns for other executives regarding financial stability or long-term support.

IBM Expert Care and partner network

IBM and its certified partners will perform all needed installations and connections to establish the Storage as a Service storage in a data center. Should any issues arise, the Expert Care solution should ensure prompt resolution.

Additionally, IBM’s worldwide network of support and resale partners can provide consulting and support on how to properly integrate and optimize the storage solution.

Storage expertise and options

IBM’s Storage as a Service incorporates IBM’s all-SSD high-performance FlashSystem. This solution also incorporates IBM’s Spectrum Virtualize to create a single virtual storage container across all of the physical drives.

In addition, customers can also tap into a number of other valuable options and technologies including:

• IBM Storage Insights: artificial intelligence (AI)-driven monitoring to aid data management and forecast data use
• IBM Spectrum Scale: Distributed file and object storage
• IBM Cyber Resiliency: Also known as Safeguarded Copy, the technology creates automated snapshot safeguarded copies on dedicated hardware
• IBM Watson: AI solutions

User reviews of IBM Storage as a Service

Customers often deploy IBM STaaS for all-flash drive arrays, which are marketed as IBM FlashSystem deployments. The IBM FlashSystem 7200 was selected as a representative product for review rating purposes.

IBM Storage as a Service pricing

IBM prices their Storage as a Service solution on a terabyte (TB)-per-month basis with one- to five-year term commitments and offers an online price estimator. Prices depend upon four tiers of service based upon performance capabilities:

• Extreme: Tier 1
  • Minimum capacity 25 TB
  • 4,500 minimum Performance Input-Output Operations per physical used TB with usage up to 85% of the usable capacity
  • 100 gigabytes per second (GBps) maximum read throughput
  • 22 GBps maximum write throughput
  • Prices start at $225 per TB per month for a five-year commitment
  • Recommended for mission critical workloads, AI, machine learning (ML), or analytics
• Premium: Tier 2
  • Minimum capacity 50 TB
  • 2,250 minimum Performance Input-Output Operations per physical used TB with usage up to 85% of the usable capacity
  • 45 GBps maximum read throughput
  • 22 GBps maximum write throughput
  • Prices start at $116 per TB per month for a five-year commitment
  • Recommended for general purpose databases, relational analytics, and virtual desktops
• Balanced: Tier 3
  • Minimum capacity 100 TB
  • 800 minimum Performance Input-Output Operations per physical used TB with usage up to 85% of the usable capacity
  • 45 GBps maximum read throughput
  • 10 GBps maximum write throughput
  • Prices start at $80 per TB per month for a five-year commitment
  • Recommended for data backup, logging, streaming content, image processing
• Capacity: Tier 4
  • Minimum capacity 100 TB
  • 60 (DRP) to 140 (regular pool) minimum Performance Input-Output Operations per physical used TB with usage up to 85% of the usable capacity
  • 19 GBps maximum read throughput
  • 6 GBps maximum write throughput
  • Prices start at $31 per TB per month for a five-year commitment

Potential customers should note that the minimum performance input-output (I/O) operations per TB can vary by workload and may not reach minimum ideal performance. Customers should use the performance numbers for the purpose of selecting the appropriate performance tier for their needs.

Similarly, read and write throughput is based on a 256 kilobyte (KB) I/O fiber channel, and all tiers are built with the goal of achieving 99.9999% uptime; 100% uptime guarantees are available for HyperSwap configurations installed by IBM Lab Services.

Prices are charged on TB of drive capacity, not TB of capacity used. Using data compression on the storage devices can effectively provide further savings on a per TB basis.

Conclusions

Not all data can move to the cloud, but data center managers may not have the option to make huge investments and tie up cash in storage hardware and on-site storage. IBM Storage as a Service provides the flexibility of OpEx payments and the scalability of the cloud for on-site data center implementations. Any data center manager looking to expand, upgrade, or replace existing storage should explore IBM STaaS solutions as an option for their local and cloud-hybrid data center needs.